Overview

Dataset statistics

Number of variables13
Number of observations8569
Missing cells15939
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory870.4 KiB
Average record size in memory104.0 B

Variable types

Categorical1
Numeric11
Unsupported1

Alerts

time has a high cardinality: 281 distinct values High cardinality
SDDY is highly correlated with GZDY and 1 other fieldsHigh correlation
XCLL is highly correlated with XLCSHigh correlation
GZDY is highly correlated with SDDY and 1 other fieldsHigh correlation
PJDY is highly correlated with SDDY and 1 other fieldsHigh correlation
XLCS is highly correlated with XCLLHigh correlation
SDDY is highly correlated with GZDY and 1 other fieldsHigh correlation
XCLL is highly correlated with LSP and 1 other fieldsHigh correlation
LSP is highly correlated with XCLLHigh correlation
GZDY is highly correlated with SDDY and 1 other fieldsHigh correlation
PJDY is highly correlated with SDDY and 1 other fieldsHigh correlation
XLCS is highly correlated with XCLLHigh correlation
SDDY is highly correlated with GZDY and 1 other fieldsHigh correlation
XCLL is highly correlated with XLCSHigh correlation
GZDY is highly correlated with SDDY and 1 other fieldsHigh correlation
PJDY is highly correlated with SDDY and 1 other fieldsHigh correlation
XLCS is highly correlated with XCLLHigh correlation
SDDY is highly correlated with LSP and 3 other fieldsHigh correlation
XCLL is highly correlated with LSPHigh correlation
LSP is highly correlated with SDDY and 1 other fieldsHigh correlation
GZDY is highly correlated with SDDY and 2 other fieldsHigh correlation
PJDY is highly correlated with SDDY and 2 other fieldsHigh correlation
number is highly correlated with SDDY and 2 other fieldsHigh correlation
SDDY has 194 (2.3%) missing values Missing
XCLL has 218 (2.5%) missing values Missing
LSP has 2113 (24.7%) missing values Missing
YHLND has 1036 (12.1%) missing values Missing
DJWD has 1997 (23.3%) missing values Missing
FZB has 1036 (12.1%) missing values Missing
GZDY has 194 (2.3%) missing values Missing
PJDY has 194 (2.3%) missing values Missing
XLCS has 194 (2.3%) missing values Missing
ZZ has 194 (2.3%) missing values Missing
list has 8569 (100.0%) missing values Missing
DJWD is highly skewed (γ1 = -52.51151413) Skewed
list is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2023-02-13 14:10:16.150822
Analysis finished2023-02-13 14:10:42.845796
Duration26.69 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

time
Categorical

HIGH CARDINALITY

Distinct281
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size67.1 KiB
2022-02-24 00:00:00
 
39
2022-02-19 00:00:00
 
39
2022-02-21 00:00:00
 
39
2022-02-22 00:00:00
 
39
2022-02-23 00:00:00
 
39
Other values (276)
8374 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-08-07 00:00:00
2nd row2021-08-09 00:00:00
3rd row2021-08-10 00:00:00
4th row2021-08-11 00:00:00
5th row2021-08-12 00:00:00

Common Values

ValueCountFrequency (%)
2022-02-24 00:00:0039
 
0.5%
2022-02-19 00:00:0039
 
0.5%
2022-02-21 00:00:0039
 
0.5%
2022-02-22 00:00:0039
 
0.5%
2022-02-23 00:00:0039
 
0.5%
2022-02-25 00:00:0039
 
0.5%
2022-02-26 00:00:0039
 
0.5%
2022-02-27 00:00:0039
 
0.5%
2022-02-28 00:00:0039
 
0.5%
2022-03-01 00:00:0039
 
0.5%
Other values (271)8179
95.4%

Length

2023-02-13T22:10:43.068614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:008569
50.0%
2022-02-2439
 
0.2%
2022-04-2039
 
0.2%
2022-04-1339
 
0.2%
2022-04-1439
 
0.2%
2022-04-1539
 
0.2%
2022-04-1639
 
0.2%
2022-04-1739
 
0.2%
2022-04-1839
 
0.2%
2022-04-1939
 
0.2%
Other values (272)8218
48.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SDDY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct131
Distinct (%)1.6%
Missing194
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean4.02483606
Minimum3.93
Maximum4.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.1 KiB
2023-02-13T22:10:43.244637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.93
5-th percentile3.945
Q13.965
median4.03
Q34.08
95-th percentile4.105
Maximum4.13
Range0.2
Interquartile range (IQR)0.115

Descriptive statistics

Standard deviation0.05867207765
Coefficient of variation (CV)0.01457750745
Kurtosis-1.525238936
Mean4.02483606
Median Absolute Deviation (MAD)0.055
Skewness-0.0472572208
Sum33708.002
Variance0.003442412695
MonotonicityNot monotonic
2023-02-13T22:10:43.431225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.951028
 
12.0%
4.08636
 
7.4%
4.09480
 
5.6%
3.94365
 
4.3%
4.1344
 
4.0%
4.085343
 
4.0%
4.095310
 
3.6%
3.97289
 
3.4%
4.065285
 
3.3%
4.02280
 
3.3%
Other values (121)4015
46.9%
ValueCountFrequency (%)
3.937
 
0.1%
3.9341
 
< 0.1%
3.94365
 
4.3%
3.9432
 
< 0.1%
3.945192
 
2.2%
3.9482
 
< 0.1%
3.951028
12.0%
3.9511
 
< 0.1%
3.9531
 
< 0.1%
3.9542
 
< 0.1%
ValueCountFrequency (%)
4.1321
 
0.2%
4.12532
 
0.4%
4.1243
 
0.5%
4.1182
 
< 0.1%
4.115139
1.6%
4.1141
 
< 0.1%
4.1121
 
< 0.1%
4.1113
 
< 0.1%
4.1196
1.1%
4.1081
 
< 0.1%

XCLL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)0.2%
Missing218
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean2740.953179
Minimum2560
Maximum2820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.1 KiB
2023-02-13T22:10:43.590476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2560
5-th percentile2650
Q12710
median2730
Q32790
95-th percentile2790
Maximum2820
Range260
Interquartile range (IQR)80

Descriptive statistics

Standard deviation58.30891582
Coefficient of variation (CV)0.02127322577
Kurtosis-0.6442398742
Mean2740.953179
Median Absolute Deviation (MAD)60
Skewness-0.6898089522
Sum22889700
Variance3399.929664
MonotonicityNot monotonic
2023-02-13T22:10:43.745348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
27903703
43.2%
27301673
19.5%
26501083
 
12.6%
2710807
 
9.4%
2820342
 
4.0%
2620265
 
3.1%
2700169
 
2.0%
2670160
 
1.9%
259084
 
1.0%
274034
 
0.4%
Other values (3)31
 
0.4%
(Missing)218
 
2.5%
ValueCountFrequency (%)
256010
 
0.1%
259084
 
1.0%
2620265
 
3.1%
264020
 
0.2%
26501083
12.6%
2670160
 
1.9%
2700169
 
2.0%
2710807
9.4%
27301673
19.5%
274034
 
0.4%
ValueCountFrequency (%)
2820342
 
4.0%
27903703
43.2%
27601
 
< 0.1%
274034
 
0.4%
27301673
19.5%
2710807
 
9.4%
2700169
 
2.0%
2670160
 
1.9%
26501083
 
12.6%
264020
 
0.2%

LSP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)0.5%
Missing2113
Missing (%)24.7%
Infinite0
Infinite (%)0.0%
Mean45.05913879
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.1 KiB
2023-02-13T22:10:43.907041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q127
median30
Q380
95-th percentile90
Maximum90
Range88
Interquartile range (IQR)53

Descriptive statistics

Standard deviation29.40898407
Coefficient of variation (CV)0.6526752367
Kurtosis-1.364994285
Mean45.05913879
Median Absolute Deviation (MAD)20
Skewness0.4772410994
Sum290901.8
Variance864.888344
MonotonicityNot monotonic
2023-02-13T22:10:44.075840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
901134
13.2%
80768
 
9.0%
28759
 
8.9%
29588
 
6.9%
27543
 
6.3%
30467
 
5.4%
9332
 
3.9%
31293
 
3.4%
70284
 
3.3%
8254
 
3.0%
Other values (21)1034
12.1%
(Missing)2113
24.7%
ValueCountFrequency (%)
26
 
0.1%
2.81
 
< 0.1%
39
 
0.1%
424
 
0.3%
522
 
0.3%
644
 
0.5%
7102
 
1.2%
8254
3.0%
9332
3.9%
10147
1.7%
ValueCountFrequency (%)
901134
13.2%
80768
9.0%
70284
 
3.3%
60108
 
1.3%
5063
 
0.7%
4014
 
0.2%
347
 
0.1%
3321
 
0.2%
32112
 
1.3%
31293
 
3.4%

YHLND
Real number (ℝ≥0)

MISSING

Distinct162
Distinct (%)2.2%
Missing1036
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean1.86610381
Minimum1.2
Maximum3.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.1 KiB
2023-02-13T22:10:44.244069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.44
Q11.65
median1.82
Q32.03
95-th percentile2.45
Maximum3.67
Range2.47
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.3136628014
Coefficient of variation (CV)0.1680843262
Kurtosis1.435330286
Mean1.86610381
Median Absolute Deviation (MAD)0.18
Skewness0.9076692507
Sum14057.36
Variance0.098384353
MonotonicityNot monotonic
2023-02-13T22:10:44.437807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.79169
 
2.0%
1.63167
 
1.9%
1.98140
 
1.6%
1.8140
 
1.6%
1.71138
 
1.6%
1.67136
 
1.6%
1.75131
 
1.5%
1.68131
 
1.5%
1.76129
 
1.5%
1.83127
 
1.5%
Other values (152)6125
71.5%
(Missing)1036
 
12.1%
ValueCountFrequency (%)
1.245
0.5%
1.2122
0.3%
1.2711
 
0.1%
1.2814
 
0.2%
1.2912
 
0.1%
1.39
 
0.1%
1.312
 
< 0.1%
1.322
 
< 0.1%
1.3311
 
0.1%
1.3414
 
0.2%
ValueCountFrequency (%)
3.671
 
< 0.1%
3.561
 
< 0.1%
3.322
 
< 0.1%
3.233
< 0.1%
3.183
< 0.1%
3.156
0.1%
3.17
0.1%
3.072
 
< 0.1%
3.024
< 0.1%
2.992
 
< 0.1%

DJWD
Real number (ℝ≥0)

MISSING
SKEWED

Distinct49
Distinct (%)0.7%
Missing1997
Missing (%)23.3%
Infinite0
Infinite (%)0.0%
Mean934.0286062
Minimum93
Maximum1001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.1 KiB
2023-02-13T22:10:44.620967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum93
5-th percentile925
Q1930
median934
Q3938
95-th percentile944
Maximum1001
Range908
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.98351873
Coefficient of variation (CV)0.01282992688
Kurtosis3693.018902
Mean934.0286062
Median Absolute Deviation (MAD)4
Skewness-52.51151413
Sum6138436
Variance143.6047211
MonotonicityNot monotonic
2023-02-13T22:10:44.800541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
934490
 
5.7%
932468
 
5.5%
933462
 
5.4%
931461
 
5.4%
936434
 
5.1%
935430
 
5.0%
930386
 
4.5%
937350
 
4.1%
929319
 
3.7%
938311
 
3.6%
Other values (39)2461
28.7%
(Missing)1997
23.3%
ValueCountFrequency (%)
931
 
< 0.1%
9161
 
< 0.1%
9172
 
< 0.1%
9181
 
< 0.1%
9197
 
0.1%
92032
 
0.4%
92132
 
0.4%
92248
0.6%
92355
0.6%
924100
1.2%
ValueCountFrequency (%)
10011
 
< 0.1%
9941
 
< 0.1%
9921
 
< 0.1%
9711
 
< 0.1%
9671
 
< 0.1%
9641
 
< 0.1%
9622
 
< 0.1%
9576
0.1%
9552
 
< 0.1%
9544
< 0.1%

FZB
Real number (ℝ≥0)

MISSING

Distinct37
Distinct (%)0.5%
Missing1036
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean2.617042347
Minimum2.46
Maximum2.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.1 KiB
2023-02-13T22:10:44.970464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.46
5-th percentile2.53
Q12.58
median2.61
Q32.65
95-th percentile2.72
Maximum2.82
Range0.36
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.05424162555
Coefficient of variation (CV)0.02072630793
Kurtosis0.7614829948
Mean2.617042347
Median Absolute Deviation (MAD)0.03
Skewness0.4715222202
Sum19714.18
Variance0.002942153942
MonotonicityNot monotonic
2023-02-13T22:10:45.142821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
2.6739
 
8.6%
2.61722
 
8.4%
2.62670
 
7.8%
2.59604
 
7.0%
2.63500
 
5.8%
2.58481
 
5.6%
2.65470
 
5.5%
2.57402
 
4.7%
2.56380
 
4.4%
2.64370
 
4.3%
Other values (27)2195
25.6%
(Missing)1036
12.1%
ValueCountFrequency (%)
2.466
 
0.1%
2.4710
 
0.1%
2.4841
 
0.5%
2.4925
 
0.3%
2.564
 
0.7%
2.5159
 
0.7%
2.5264
 
0.7%
2.53120
1.4%
2.5493
1.1%
2.55205
2.4%
ValueCountFrequency (%)
2.827
 
0.1%
2.815
 
0.1%
2.811
 
0.1%
2.7910
 
0.1%
2.7833
 
0.4%
2.7716
 
0.2%
2.7628
 
0.3%
2.7544
0.5%
2.7494
1.1%
2.7372
0.8%

GZDY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct370
Distinct (%)4.4%
Missing194
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean4.070502567
Minimum2.265
Maximum4.754
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.1 KiB
2023-02-13T22:10:45.320907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.265
5-th percentile3.968
Q14.009
median4.072
Q34.128
95-th percentile4.18
Maximum4.754
Range2.489
Interquartile range (IQR)0.119

Descriptive statistics

Standard deviation0.07316633188
Coefficient of variation (CV)0.01797476618
Kurtosis44.09093041
Mean4.070502567
Median Absolute Deviation (MAD)0.059
Skewness-1.60320741
Sum34090.459
Variance0.00535331212
MonotonicityNot monotonic
2023-02-13T22:10:45.511751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.14561
 
0.7%
4.12656
 
0.7%
4.11756
 
0.7%
4.1454
 
0.6%
4.13154
 
0.6%
3.98753
 
0.6%
3.99753
 
0.6%
4.00153
 
0.6%
4.13852
 
0.6%
3.99852
 
0.6%
Other values (360)7831
91.4%
(Missing)194
 
2.3%
ValueCountFrequency (%)
2.2651
< 0.1%
3.861
< 0.1%
3.8712
< 0.1%
3.8761
< 0.1%
3.8821
< 0.1%
3.8842
< 0.1%
3.8851
< 0.1%
3.8892
< 0.1%
3.891
< 0.1%
3.8931
< 0.1%
ValueCountFrequency (%)
4.7541
< 0.1%
4.3471
< 0.1%
4.321
< 0.1%
4.3171
< 0.1%
4.3161
< 0.1%
4.3021
< 0.1%
4.2992
< 0.1%
4.2921
< 0.1%
4.2911
< 0.1%
4.291
< 0.1%

PJDY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct335
Distinct (%)4.0%
Missing194
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean4.068954746
Minimum2.265
Maximum4.754
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.1 KiB
2023-02-13T22:10:45.691959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.265
5-th percentile3.969
Q14.009
median4.072
Q34.125
95-th percentile4.168
Maximum4.754
Range2.489
Interquartile range (IQR)0.116

Descriptive statistics

Standard deviation0.06984958066
Coefficient of variation (CV)0.01716646781
Kurtosis52.98699953
Mean4.068954746
Median Absolute Deviation (MAD)0.057
Skewness-1.944666258
Sum34077.496
Variance0.004878963918
MonotonicityNot monotonic
2023-02-13T22:10:45.876258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.11770
 
0.8%
4.13164
 
0.7%
4.13764
 
0.7%
4.12162
 
0.7%
4.13262
 
0.7%
4.1261
 
0.7%
4.12461
 
0.7%
4.12861
 
0.7%
4.1360
 
0.7%
4.12757
 
0.7%
Other values (325)7753
90.5%
(Missing)194
 
2.3%
ValueCountFrequency (%)
2.2651
< 0.1%
3.861
< 0.1%
3.8712
< 0.1%
3.8781
< 0.1%
3.8841
< 0.1%
3.8851
< 0.1%
3.8891
< 0.1%
3.891
< 0.1%
3.8971
< 0.1%
3.8991
< 0.1%
ValueCountFrequency (%)
4.7541
< 0.1%
4.3471
< 0.1%
4.321
< 0.1%
4.3161
< 0.1%
4.3031
< 0.1%
4.3021
< 0.1%
4.292
< 0.1%
4.2841
< 0.1%
4.2731
< 0.1%
4.2691
< 0.1%

XLCS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct719
Distinct (%)8.6%
Missing194
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean1762.579343
Minimum0
Maximum2352
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size67.1 KiB
2023-02-13T22:10:46.053758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1574
Q11681
median1756
Q31840
95-th percentile1982.3
Maximum2352
Range2352
Interquartile range (IQR)159

Descriptive statistics

Standard deviation132.5019366
Coefficient of variation (CV)0.07517501955
Kurtosis19.12434118
Mean1762.579343
Median Absolute Deviation (MAD)79
Skewness-1.246276278
Sum14761602
Variance17556.7632
MonotonicityNot monotonic
2023-02-13T22:10:46.419501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175441
 
0.5%
173141
 
0.5%
173740
 
0.5%
167140
 
0.5%
173840
 
0.5%
178538
 
0.4%
177336
 
0.4%
173635
 
0.4%
174034
 
0.4%
176834
 
0.4%
Other values (709)7996
93.3%
(Missing)194
 
2.3%
ValueCountFrequency (%)
04
< 0.1%
71
 
< 0.1%
7851
 
< 0.1%
9941
 
< 0.1%
10151
 
< 0.1%
10951
 
< 0.1%
11671
 
< 0.1%
11871
 
< 0.1%
12221
 
< 0.1%
12561
 
< 0.1%
ValueCountFrequency (%)
23521
< 0.1%
22951
< 0.1%
22371
< 0.1%
22241
< 0.1%
22212
< 0.1%
22171
< 0.1%
22121
< 0.1%
22001
< 0.1%
21911
< 0.1%
21882
< 0.1%

ZZ
Real number (ℝ≥0)

MISSING

Distinct41
Distinct (%)0.5%
Missing194
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean11.63438806
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.1 KiB
2023-02-13T22:10:46.596177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q19
median11
Q313
95-th percentile21
Maximum56
Range55
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.470727126
Coefficient of variation (CV)0.3842683519
Kurtosis5.172523215
Mean11.63438806
Median Absolute Deviation (MAD)2
Skewness1.766666583
Sum97438
Variance19.98740103
MonotonicityNot monotonic
2023-02-13T22:10:46.766803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
91135
13.2%
101061
12.4%
81033
12.1%
11867
10.1%
12763
8.9%
7657
7.7%
13538
6.3%
14417
 
4.9%
15304
 
3.5%
16280
 
3.3%
Other values (31)1320
15.4%
ValueCountFrequency (%)
15
 
0.1%
21
 
< 0.1%
41
 
< 0.1%
527
 
0.3%
6256
 
3.0%
7657
7.7%
81033
12.1%
91135
13.2%
101061
12.4%
11867
10.1%
ValueCountFrequency (%)
561
 
< 0.1%
422
 
< 0.1%
401
 
< 0.1%
391
 
< 0.1%
382
 
< 0.1%
372
 
< 0.1%
362
 
< 0.1%
355
0.1%
344
< 0.1%
334
< 0.1%

number
Real number (ℝ≥0)

HIGH CORRELATION

Distinct39
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5066.964873
Minimum5048
Maximum5086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.1 KiB
2023-02-13T22:10:46.936292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5048
5-th percentile5050
Q15057
median5067
Q35077
95-th percentile5084
Maximum5086
Range38
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.15463223
Coefficient of variation (CV)0.002201442581
Kurtosis-1.176528849
Mean5066.964873
Median Absolute Deviation (MAD)10
Skewness-0.01898982193
Sum43418822
Variance124.4258201
MonotonicityIncreasing
2023-02-13T22:10:47.118296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
5084277
 
3.2%
5058271
 
3.2%
5061270
 
3.2%
5074268
 
3.1%
5066268
 
3.1%
5071266
 
3.1%
5050265
 
3.1%
5083265
 
3.1%
5081264
 
3.1%
5077263
 
3.1%
Other values (29)5892
68.8%
ValueCountFrequency (%)
5048215
2.5%
5049212
2.5%
5050265
3.1%
5051254
3.0%
5052175
2.0%
5053182
2.1%
5054251
2.9%
5055166
1.9%
5056244
2.8%
5057182
2.1%
ValueCountFrequency (%)
5086170
2.0%
5085173
2.0%
5084277
3.2%
5083265
3.1%
5082179
2.1%
5081264
3.1%
5080161
1.9%
5079181
2.1%
5078224
2.6%
5077263
3.1%

list
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing8569
Missing (%)100.0%
Memory size67.1 KiB

Interactions

2023-02-13T22:10:39.943197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:22.091932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:24.414763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:26.100334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:27.779979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:29.558575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:31.221035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:32.941383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:34.714974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:36.387005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:38.088227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:40.109875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:22.699517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:24.572682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:26.258354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:28.036013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:29.718718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:31.386012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:33.207469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:34.874490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:36.548931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:38.250810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:40.267177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:22.856729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:24.721287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:26.410264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:28.199263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-02-13T22:10:33.358877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-02-13T22:10:24.867477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:26.561190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:28.350320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:30.011948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:31.698325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:33.505444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:35.175778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:36.856140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:38.565352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:40.573662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:23.166533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:25.014534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:26.709219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:28.496927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:30.157681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:31.855903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:33.653975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:35.325151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:37.004924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:38.725923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:40.729967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:23.322558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:25.163667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:26.853702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:28.645675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:30.305048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:32.007687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:33.800933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:35.471425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:37.156635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:38.883885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:40.891325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:23.484565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:25.321073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:27.010248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:28.802465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:30.459973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:32.166180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:33.956813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:35.626865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:37.312834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:39.042240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:41.047987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:23.640149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:25.472387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:27.159848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:28.953187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:30.609768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:32.320773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:34.107295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:35.775729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:37.464182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:39.193545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:41.202693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:23.879869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:25.636950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:27.311368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:29.102472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:30.757881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:32.473125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:34.253229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:35.926031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:37.612208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:39.345272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:41.355458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:24.037505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:25.794531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:27.466622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:29.252470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:30.910386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:32.625635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:34.402378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:36.072522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:37.759518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:39.633745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:41.520976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:24.246379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:25.943388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:27.625429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:29.402838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:31.065310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:32.781256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:34.555995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:36.229248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:37.911348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T22:10:39.785971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-02-13T22:10:47.285078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-02-13T22:10:47.565230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-02-13T22:10:47.762225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-02-13T22:10:47.959788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-02-13T22:10:41.809024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-13T22:10:42.029699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-13T22:10:42.449563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-02-13T22:10:42.690258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

timeSDDYXCLLLSPYHLNDDJWDFZBGZDYPJDYXLCSZZnumberlist
02021-08-07 00:00:004.0652620.0NaN1.71NaN2.614.0764.0761525.08.05048NaN
12021-08-09 00:00:004.0652620.090.01.70928.02.654.1074.0931487.011.05048NaN
22021-08-10 00:00:004.0652650.060.01.70928.02.654.0964.0801711.09.05048NaN
32021-08-11 00:00:004.0652620.070.01.70932.02.654.1124.0801586.07.05048NaN
42021-08-12 00:00:004.0652620.090.01.70928.02.654.0944.0931450.08.05048NaN
52021-08-13 00:00:004.0652620.070.01.70922.02.654.0934.0891520.09.05048NaN
62021-08-14 00:00:004.0652650.080.01.70923.02.654.0954.0961538.07.05048NaN
72021-08-15 00:00:004.0652650.080.01.70927.02.654.1344.0941503.09.05048NaN
82021-08-16 00:00:004.0652650.070.01.63926.02.654.1054.0831624.08.05048NaN
92021-08-17 00:00:004.0652650.080.01.63926.02.654.0904.0901445.08.05048NaN

Last rows

timeSDDYXCLLLSPYHLNDDJWDFZBGZDYPJDYXLCSZZnumberlist
85592022-05-13 00:00:004.022790.027.01.49932.02.644.0464.0471849.012.05086NaN
85602022-05-14 00:00:004.022790.028.01.49928.02.644.0434.0461838.014.05086NaN
85612022-05-15 00:00:004.022790.027.01.49929.02.644.0394.0451808.011.05086NaN
85622022-05-16 00:00:004.022790.027.01.49938.02.644.0604.0681921.013.05086NaN
85632022-05-17 00:00:004.022790.027.01.49938.02.644.0714.0961764.012.05086NaN
85642022-05-18 00:00:004.022790.027.01.95930.02.634.0734.0781855.015.05086NaN
85652022-05-19 00:00:004.022790.027.01.95931.02.634.0604.0601829.020.05086NaN
85662022-05-20 00:00:004.022790.027.01.95931.02.634.0584.0741960.09.05086NaN
85672022-05-21 00:00:004.022790.027.01.95933.02.634.0514.0511920.010.05086NaN
85682022-05-22 00:00:004.022790.027.01.95935.02.634.0594.0601799.010.05086NaN